add marshalling (#28)

DESCRIPTION

@@ -23,7 +23,7 @@ Imports:
     checkmate,
     data.table,
     lgr,
-    mlr3 (>= 0.17.0),
+    mlr3 (>= 0.19.0),
     mlr3misc,
     uuid
 Suggests:

NEWS.md

@@ -3,6 +3,7 @@
 * feat: `reduceResultsBatchmark` gains argument `fun` which is passed on to `batchtools::reduceResultsList`, useful for deleting model data to avoid running out of memory, https://github.com/mlr-org/mlr3batchmark/issues/18 Thanks to Toby Dylan Hocking @tdhock for the PR.
 * docs: A warning is now given when the loaded mlr3 version differs from the
 mlr3 version stored in the trained learners
+* Support marshaling
 
 # mlr3batchmark 0.1.1
 

R/reduceResultsBatchmark.R

@@ -14,7 +14,8 @@
 #'
 #' @return [mlr3::BenchmarkResult].
 #' @export
-reduceResultsBatchmark = function(ids = NULL, store_backends = TRUE, reg = batchtools::getDefaultRegistry(), fun=NULL) { # nolint
+reduceResultsBatchmark = function(ids = NULL, store_backends = TRUE, reg = batchtools::getDefaultRegistry(), fun = NULL, unmarshal = TRUE) { # nolint
+  assert_flag(unmarshal)
   if (is.null(ids)) {
     ids = batchtools::findDone(ids, reg = reg)
   } else {
@@ -87,5 +88,9 @@ reduceResultsBatchmark = function(ids = NULL, store_backends = TRUE, reg = batch
     bmr$combine(mlr3::BenchmarkResult$new(rdata))
   }
 
+  if (unmarshal) {
+    bmr$unmarshal()
+  }
+
   return(bmr)
 }

R/worker.R

@@ -9,6 +9,7 @@ run_learner = function(job, data, learner_hash, store_models, ...) {
     learner = learner,
     resampling = resampling,
     store_models = store_models,
-    lgr_threshold = lgr::get_logger("mlr3")$threshold
+    lgr_threshold = lgr::get_logger("mlr3")$threshold,
+    is_sequential = FALSE
   )
 }

tests/testthat/test_batchmark.R

@@ -86,3 +86,14 @@ test_that("failing jobs", {
   expect_data_table(as.data.table(results), nrow = 8L)
   expect_error(reduceResultsBatchmark(reg = reg, ids = ids), "successfully computed")
 })
+
+test_that("marshaling", {
+  reg = batchtools::makeExperimentRegistry(NA)
+  batchmark(benchmark_grid(tsk("iris"), lrn("classif.debug"), rsmp("holdout")), store_models = TRUE)
+  submitJobs()
+  bmr_unmarshaled = reduceResultsBatchmark(unmarshal = TRUE)
+  bmr_marshaled = reduceResultsBatchmark(unmarshal = FALSE)
+
+  expect_true(bmr_marshaled$resample_result(1)$learners[[1]]$marshaled)
+  expect_false(bmr_unmarshaled$resample_result(1)$learners[[1]]$marshaled)
+})